6 found
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Edward C. Merrill [3]Edward Merrill [3]
  1.  15
    Sex Differences in Using Spatial and Verbal Abilities Influence Route Learning Performance in a Virtual Environment: A Comparison of 6- to 12-Year Old Boys and Girls. [REVIEW]Edward C. Merrill, Yingying Yang, Beverly Roskos & Sara Steele - 2016 - Frontiers in Psychology 7.
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  2.  6
    Patterns of Differences in Wayfinding Performance and Correlations Among Abilities Between Persons with and Without Down Syndrome and Typically Developing Children.Megan Davis, Edward C. Merrill, Frances A. Conners & Beverly Roskos - 2014 - Frontiers in Psychology 5.
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  3. Bottom-Up Skill Learning in Reactive Sequential Decision Tasks.Ron Sun, Todd Peterson & Edward Merrill - unknown
    This paper introduces a hybrid model that unifies connectionist, symbolic, and reinforcement learning into an integrated architecture for bottom-up skill learning in reactive sequential decision tasks. The model is designed for an agent to learn continuously from on-going experience in the world, without the use of preconceived concepts and knowledge. Both procedural skills and high-level knowledge are acquired through an agent’s experience interacting with the world. Computational experiments with the model in two domains are reported.
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  4.  4
    The Acquisition of Survey Knowledge by Individuals With Down Syndrome.Zachary M. Himmelberger, Edward C. Merrill, Frances A. Conners, Beverly Roskos, Yingying Yang & Trent Robinson - 2020 - Frontiers in Human Neuroscience 14.
  5. A Bottom-Up Model of Skill Learning.Ron Sun, Todd Peterson & Edward Merrill - unknown
    We present a skill learning model CLARION. Different from existing models of high-level skill learning that use a topdown approach (that is, turning declarative knowledge into procedural knowledge), we adopt a bottom-up approach toward low-level skill learning, where procedural knowledge develops first and declarative knowledge develops later. CLAR- ION is formed by integrating connectionist, reinforcement, and symbolic learning methods to perform on-line learning. We compare the model with human data in a minefield navigation task. A match between the model and (...)
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  6. Tuscaloosa, AL 35487.Todd Peterson, Ron Sun & Edward Merrill - unknown
    This paper introduces a hybrid model that combines connectionist, symbolic, and reinforcement learning for tackling reactive sequential decision tasks by a situated agent. Both procedural skills and high-level symbolic representations are acquired through an agent's experience interacting with the world, in a bottom-up direction. It deals with on-line learning, that is, learning continuously from on-going experience in the world, without the use of preconstructed data sets or preconceived concepts. The model is a connectionist one based on a two-level approach proposed (...)
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